Final Project
Lakna Perera
10/4/2021
Reported cases as a percentage of total population
cases_deaths_world <- covnat_weekly
df1 <- cases_deaths_world %>%
filter(date == max(date), !is.na(pop)) %>%
mutate(pop_percentage_cases = round((cu_cases/pop)*100, digits = 2),
mytext = paste("Country: ", cname, "\n",
"Percentage: ", pop_percentage_cases, "%", sep=""))
plot_ly(df1, type='choropleth',
locations=df1$iso3,
z=df1$pop_percentage_cases,
text = df1$mytext,
colorscale= "#de2d26") %>%
layout(title = "Percentage of Reported Covid-19 Cases")Interpretation
Generally, the number of reported Covid-19 cases for each country is relative to the population of the respective country. Therefore, it is insightful to analyse the reported cases relative to the total population of each country.
This graph shows Covid-19 reported cases as a percentage to the total population of different countries in the world.
The percentage of infected people varies between zero and 20.01%.
The maximum percentage is recorded as Seychelles with a value of 20.01%.
Higher percentages are indicated by darker colors.
Percentage of reported Covid-19 cases for USA is recorded as 11.39%.
Percentage of reported Covid-19 cases is lower in Sri Lanka compared to the other countries.
However, countries such as Micronesia, Samoa, Soloman Islands and Vanuatu with zero cases of Covid-19 may not indicate that there is no Covid-19 cases in those countries. This may be due to lack of Covid-19 screening tests done in those countries.
Statewise analysis of reported Covid-19 cases in USA
df4 <- nytcovcounty
df4 %>%
mutate(ym = format(date, '%Y-%m')) %>%
group_by(ym, state) %>%
summarize(ym_sum = sum(cases)) %>%
ggplot(aes(x = ym, fill = ym_sum, y = reorder(state,ym_sum))) +
geom_raster()+
scale_fill_viridis_c() +
ggtitle("Reported Covid-19 cases by State in USA") +
labs(x = "Month", y = "State Name")
Interpretation
This graph shows reported Covid-19 cases aggregated monthly for each state in USA.
In January 2020 only California, Illinois, Arizona and Washington had reported Covid-19 cases.
In February another six states namely Texas, Wisconsin, Massachusetts, Utah, Nebraska and Oregon were reported with Covid-19 cases.
From March 2020 the Covid-19 virus started to spread in almost every state in USA.
California is reported as the state with highest cases of Covid-19 followed by Texas, Florida and New York respectively.
The reported cases in the period 2020 December to 2021 August are comparatively higher.
The highest Covid-19 cases were also reported in California in August 2021.
Analysis of Covid-19 deaths by age in USA
df2 <- nchs_sas
age_group_names <- c("Under 1 year","1-4 years","5-14 years","15-24 years",
"25-34 years","35-44 years","45-54 years","55-64 years",
"65-74 years","75-84 years","85 years and over")
selected_data <- df2 %>%
filter(group == "By Month",
sex == "All Sexes",
age_group %in% age_group_names,
state != "United States") # Remove data stored
# as United States (repeated values)
# Assign zero to missing values in covid-19 deaths
selected_data$covid_19_deaths[is.na(selected_data$covid_19_deaths)] <- 0
selected_data %>%
filter(covid_19_deaths > 0) %>%
mutate(age_group = as.factor(age_group)) %>%
ggplot(aes(x = covid_19_deaths,
y = age_group,
fill = age_group)) +
geom_density_ridges(position = "raincloud",
jittered_points = TRUE) +
coord_cartesian(xlim = c(0, 300)) +
labs(title = "Distribution of Monthly Covid-19 Deaths by Age in USA",
x = "Number of Covid-19 Deaths",
y = "Age Group") +
theme_bw()
Interpretation
This graph shows reported Covid-19 deaths aggregated monthly under each age category in USA.
There were no reported Covid-19 deaths below the age of 15.
Most of the monthly death counts are within the range of 1 - 100 for every age group.
The variance of the reported monthly death counts is increasing with the age. For an instance Covid-19 deaths in the age group 15 -24 vary between 1 to 50 but for the people who are aged 85 and above has reported monthly deaths over 300.
Also this graph clearly shows that age is a critical parameter which determines the deaths due to Covid-19.
Analysis of reported Covid-19 deaths, Pneumonia deaths and Influenza deaths in USA
selected_data_2 <- df2 %>%
filter(group == "By Month",
sex %in% c("Female", "Male"),
age_group %in% age_group_names,
state != "United States") # Remove data stored
# under United States (repeated values)
# Assign zero to missing values
selected_data_2$covid_19_deaths[is.na(selected_data_2$covid_19_deaths)] <- 0
selected_data_2$pneumonia_deaths[is.na(selected_data_2$pneumonia_deaths)] <- 0
selected_data_2$influenza_deaths[is.na(selected_data_2$influenza_deaths)] <- 0
selected_data_2 %>%
filter(covid_19_deaths > 0, pneumonia_deaths > 0, influenza_deaths > 0) %>%
mutate(age_group = as.factor(age_group),
sex = as.factor(sex)) %>%
ggpairs(mapping = aes(color=sex, alpha =0.2),
columns =c ("covid_19_deaths", "pneumonia_deaths", "influenza_deaths"))+
ggtitle("Scatter plot matrix for Covid-19, Pneumonia and Influenza Deaths")
Interpretation
This graph shows the information about the pearson correlation coefficients, scatter plots and individual distributions for both male and female deaths reported under as Covid-19, Pneumonia and Influenza.
According to the individual distributions in the plot, both female and male deaths due to Covid-19, Pneumonia and Influenza are distributed in similar way.
Overall pearson correlation for Covid-19 and Pneumonia is 0.937 which indicates that there is a strong correlation with the deaths reported as Covid-19 and Pneumonia.
There is a small relationship between deaths reported as Covid-19 and Influenza as well. However, it is not a strong relationship as Covid-19 and Pneumonia.